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All Journal Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi)
Erika Nurkholisah
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Computer Science & IT

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Penerapan Text Mining untuk Pengelolaan dan Klasifikasi Informasi Berita Online Menggunakan Algoritma Naïve Bayes Alfitianti Azahra; Qonita Luthfiyah; Erika Nurkholisah; Kartika
Prosiding SISFOTEK Vol 9 No 1 (2025): SISFOTEK IX 2025
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Negative reporting can cause a decline in public trust in public officials and news hoaxes can also cause divisions among local residents. Naïve Bayes is a simple probabilistic classifier that calculates a set of probabilities by adding up the frequencies and combinations of values ??from the given dataset, the accuracy results are obtained at 61.22% with this being explained in detail, namely Prediction of news results with the Neutral Category and it turns out to be true Neutral as many as 27 data. Predicted news results with a Neutral Category and turned out to be true positive as many as 23 data. Prediction of news results with a Neutral Category and it turns out to be true Negative as much as 1 Data. Predicted news results with a Positive Category and turned out to be true Neutral as many as 14 Data. Predicted news results with positive categories and turned out to be true positive as many as 33 data. Prediction of news results with a positive category and it turns out to be true negative as much as 0 data. Prediction of news results with a Negative Category and turns out to be true Neutral as much as 0 Data. Prediction of news results with a Negative Category and it turns out to be true Positive as much as 0 Data. Prediction of news results with a Negative Category and it turns out to be true Negative as much as 0 Data.
Co-Authors Alfitianti Azahra Kartika Qonita Luthfiyah